The Hessian bulk consists of weakly broken continuous symmetries of the network parametrization, exact zeros in linear nets that ReLU lifts as pseudo-Goldstone modes whose eigenvectors stay in the symmetry subspace.
Kawaguchi, Deep Learning without Poor Local Min- ima, inAdvances in Neural Information Processing Sys- tems, Vol
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Explaining Near-Zero Hessian Eigenvalues Through Approximate Symmetries in Neural Networks
The Hessian bulk consists of weakly broken continuous symmetries of the network parametrization, exact zeros in linear nets that ReLU lifts as pseudo-Goldstone modes whose eigenvectors stay in the symmetry subspace.